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Novel cultivation technologies demand the adaptation of existing analytical concepts. Metabolic flux analysis (MFA) requires stable-isotope labeling of biomass-bound protein as the primary information source. Obtaining the required protein in cultivation set-ups where biomass is inaccessible due to low cell densities and cell immobilization is difficult to date. We developed a non-disruptive analytical concept for 13 C-based metabolic flux analysis based on secreted protein as an information carrier for isotope mapping in the protein-bound amino acids. This "metabolic flux probe" (MFP) concept was investigated in different cultivation set-ups with a recombinant, protein-secreting yeast strain. The obtained results grant insight into intracellular protein turnover dynamics. Experiments under metabolic but isotopically nonstationary conditions in continuous glucose-limited chemostats at high dilution rates demonstrated faster incorporation of isotope information from labeled glucose into the recombinant reporter protein than in biomass-bound protein. Our results suggest that the reporter protein was polymerized from intracellular amino acid pools with higher turnover rates than biomass-bound protein. The latter aspect might be vital for 13 C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics.

13 C-based metabolic flux analysis ( 13 C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In 13 C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13 C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13 C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Metabolic flux analysis based on 13 C-derived constraints has proved to be a powerful method for quantitative physiological characterisation of one of the most extensively used microbial cell factory platforms, Pichia pastoris (syn. Komagataella spp.). Nonetheless, the reduced number of carbon atoms and the symmetry of the glycerol molecule has hampered the comprehensive determination of metabolic fluxes when used as the labelled C-source. Moreover, metabolic models typically used for 13 C-based flux balance analysis may be incomplete or misrepresent the actual metabolic network. To circumvent these limitations, we reduced the genome-scale metabolic model iMT1026-v3.0 into a core model and used it for the iterative fitting of metabolic fluxes to the measured mass isotope distribution of proteinogenic amino acids obtained after fractional 13 C labelling of cells with [1,3- 13 C]-glycerol. This workflow allows reliable estimates to be obtained for in vivo fluxes in P. pastoris cells growing on glycerol as sole carbon source, as well as revising previous assumptions concerning its metabolic operation, such as alternative metabolic branches, calculation of energetic parameters and proposed specific cofactor utilisation., (Copyright © 2019 Elsevier B.V. All rights reserved.)

A novel analytical approach based on liquid chromatography coupled to quadrupole time of flight mass spectrometry, employing data-dependent triggering for analysis of isotopologue and tandem mass isotopomer fractions of metabolites of the primary carbon metabolism was developed. The implemented QTOFMS method employs automated MS/MS triggering of higher abundant, biologically relevant isotopologues for generating positional information of the respective metabolite. Using this advanced isotopologue selective fragmentation approach enables the generation of significant tandem mass isotopomer data within a short cycle time without compromising sensitivity. Due to a lack of suitable reference material certified for isotopologue ratios, a Pichia pastoris cell extract with a defined C distribution as well as a cell extract from a C-based metabolic flux experiment were employed for proof of concept. Moreover, a method inter-comparison with an already established GC-CI-(Q)TOFMS approach was conducted. Both methods showed good agreement on isotopologue and tandem mass isotopomer distributions for the two different cell extracts. [ABSTRACT FROM AUTHOR]

For the study of different levels of (intra)cellular regulation and condition-dependent insight into metabolic activities, fluxomics experiments based on stable isotope tracer experiments using 13 C have become a well-established approach. The experimentally obtained non-naturally distributed 13 C labeling patterns of metabolite pools can be measured by mass spectrometric detection with front-end separation and can be consequently incorporated into biochemical network models. Here, despite a tedious derivatization step, gas chromatographic separation of polar metabolites is favorable because of the wide coverage range and high isomer separation efficiency. However, the typically employed electron ionization energy of 70 eV leads to significant fragmentation and consequently only low-abundant ions with an intact carbon backbone. Since these ions are considered a prerequisite for the analysis of the non-naturally distributed labeling patterns and further integration into modeling strategies, a softer ionization technique is needed. In the present work, a novel low energy electron ionization source is optimized for the analysis of primary metabolites and compared with a chemical ionization approach in terms of trueness, precision, and sensitivity.

In the field of metabolic engineering 13 C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a 13 C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation. Graphical abstract ᅟ.

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Several bacterial species and filamentous fungi utilize the phosphoketolase pathway (PHK) for glucose dissimilation as an alternative to the Embden-Meyerhof-Parnas pathway. In Aspergillus nidulans, the utilization of this metabolic pathway leads to increased carbon flow towards acetate and acetyl CoA. In the first step of the PHK, the pentose phosphate pathway intermediate xylulose-5-phosphate is converted into acetylphosphate and glyceraldehyde-3-phosphate through the action of xylulose-5-phosphate phosphoketolase, and successively acetylphosphate is converted into acetate by the action of acetate kinase. In the present work, we describe a metabolic engineering strategy used to express the fungal genes of the phosphoketolase pathway in Saccharomyces cerevisiae and the effects of the expression of this recombinant route in yeast. The phenotype of the engineered yeast strain MP003 was studied during batch and chemostat cultivations, showing a reduced biomass yield and an increased acetate yield during batch cultures. To establish whether the observed effects in the recombinant strain MP003 were due directly or indirectly to the expression of the phosphoketolase pathway, we resolved the intracellular flux distribution based on C labeling during chemostat cultivations. From flux analysis it is possible to conclude that yeast is able to use the recombinant pathway. Our work indicates that the utilization of the phosphoketolase pathway does not interfere with glucose assimilation through the Embden-Meyerhof-Parnas pathway and that the expression of this route can contribute to increase the acetyl CoA supply, therefore holding potential for future metabolic engineering strategies having acetyl CoA as precursor for the biosynthesis of industrially relevant compounds. [ABSTRACT FROM AUTHOR]

Abstract: Ethanoligenens harbinense is a promising hydrogen producing microorganism due to its high inherent hydrogen production rate. Even though the effect of media optimization and inhibitory metabolites has been studied in order to improve the hydrogen productivity of these cultures, the identification of the underlying causes of the observed changes in productivity has not been targeted to date. In this work we present a genome based metabolic flux analysis (MFA) framework, for the comprehensive study of E. harbinense in culture, and the effect of inhibitory metabolites and media composition on its metabolic state. A metabolic model was constructed for E. harbinense based on its annotated genome sequence and proteomic evidence. This model was employed to perform MFA and obtain the intracellular flux distribution under different culture conditions. These results allow us to identify key elements in the metabolism that can be associated to the observed production phenotypes, and that can be potential targets for metabolic engineering in order to enhanced hydrogen production in E. harbinense. [Copyright &y& Elsevier]

The measurement of site-specific (13)C enrichments in complex mixtures of (13)C-labeled metabolites is a powerful tool for metabolic flux analysis. One of the main methods to measure such enrichments is homonuclear (1)H 2D NMR. However, the major limitation of this technique is the acquisition time, which can amount to a few hours. This drawback was recently overcome by the design of fast COSY experiments for measuring specific (13)C-enrichments, based on single-scan 2D NMR. However, these experiments are still limited by overlaps because of(1)H-(13)C splittings, thus limiting the metabolic information accessible for complex biological mixtures. To circumvent this limitation, we propose to tilt the (1)H-(13)C coupling into a third dimension via fast-hybrid 3D NMR methods combining the speed of ultrafast 2D NMR with the high resolution of conventional methods. Two strategies are described that allow the acquisition of a complete 3D J-resolved-COSY spectrum in 12 min (for concentrations as low as 10 mM). The analytical potentialities of both methods are evaluated on a series of (13)C-enriched glucose samples and on a biomass hydrolyzate obtained from Escherichia coli cells. Once optimized, the two complementary experiments lead to a trueness and a precision of a few percent and an excellent linearity. The advantages and drawbacks of these approaches are discussed and their potentialities are highlighted.

In a (13)C experiment for metabolic flux analysis ((13)C MFA), we examined isotope discrimination by measuring the labeling of glucose, amino acids, and hexose monophosphates via mass spectrometry. When Escherichia coli grew in a mix of 20% fully labeled and 80% naturally labeled glucose medium, the cell metabolism favored light isotopes and the measured isotopic ratios (δ(13)C) were in the range of -35 to -92. Glucose transporters might play an important role in such isotopic fractionation. Flux analysis showed that both isotopic discrimination and isotopic impurities in labeled substrates could affect the solution of (13)C MFA., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: Constraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements., Results: Herein we discuss a possibilistic framework to perform metabolic flux estimations using a constraint-based model and a set of measurements. The methodology is able to handle inconsistencies, by considering sensors errors and model imprecision, to provide rich and reliable flux estimations. The methodology can be cast as linear programming problems, able to handle thousands of variables with efficiency, so it is suitable to deal with large-scale networks. Moreover, the possibilistic estimation does not attempt necessarily to predict the actual fluxes with precision, but rather to exploit the available data--even if those are scarce--to distinguish possible from impossible flux states in a gradual way., Conclusion: We introduce a possibilistic framework for the estimation of metabolic fluxes, which is shown to be flexible, reliable, usable in scenarios lacking data and computationally efficient.

Stable isotope, and in particular (13)C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on (13)C-labeled glucose and subsequent gas chromatography mass spectrometric detection of (13)C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on (13)C-pattern in free intracellular metabolites. The presented protocols take 5-10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.

Corynebacterium glutamicum is intensively used for the industrial large-scale (fed-) batch production of amino acids, especially glutamate and lysine. However, metabolic flux analyses based on 13C-labeling experiments of this organism have hitherto been restricted to small-scale batch conditions and carbon-limited chemostat cultures, and are therefore of questionable relevance for industrial fermentations. To lever flux analysis to the industrial level, a novel Sensor Reactor approach was developed (El Massaoudi et al., Metab. Eng., submitted), in which a 300-L production reactor and a 1-L Sensor Reactor are run in parallel master/slave modus, thus enabling 13C-based metabolic flux analysis to generate a series of flux maps that document large-scale fermentation courses in detail. We describe the successful combination of this technology with nuclear magnetic resonance (NMR) analysis, metabolite balancing methods and a mathematical description of 13C-isotope labelings resulting in a powerful tool for quantitative pathway analysis during a batch fermentation. As a first application, 13C-based metabolic flux analysis was performed on exponentially growing, lysine-producing C. glutamicum MH20-22B during three phases of a pilot-scale batch fermentation. By studying the growth, (co-) substrate consumption and (by-) product formation, the similarity of the fermentations in production and Sensor Reactor was verified. Applying a generally applicable mathematical model, which included metabolite and carbon labeling balances for the analysis of proteinogenic amino acid 13C-isotopomer labeling data, the in vivo metabolic flux distribution was investigated during subsequent phases of exponential growth. It was shown for the first time that the in vivo reverse C(4)-decarboxylation flux at the anaplerotic node in C. glutamicum significantly decreased (70%) in parallel with threefold increased lysine formation during the investigated subsequent phases of exponential growth.

Abstract Background The Gram-positive Streptomyces lividans TK24 is an attractive host for heterologous protein production because of its high capability to secrete proteins—which favors correct folding and facilitates downstream processing—as well as its acceptance of methylated DNA and its low endogeneous protease activity. However, current inconsistencies in protein yields urge for a deeper understanding of the burden of heterologous protein production on the cell. In the current study, transcriptomics and $$^{13}\hbox {C}$$ 13C -based fluxomics were exploited to uncover gene expression and metabolic flux changes associated with heterologous protein production. The Rhodothermus marinus thermostable cellulase A (CelA)—previously shown to be successfully overexpressed in S. lividans—was taken as an example protein. Results RNA-seq and $$^{13}\hbox {C}$$ 13C -based metabolic flux analysis were performed on a CelA-producing and an empty-plasmid strain under the same conditions. Differential gene expression, followed by cluster analysis based on co-expression and co-localization, identified transcriptomic responses related to secretion-induced stress and DNA damage. Furthermore, the OsdR regulon (previously associated with hypoxia, oxidative stress, intercellular signaling, and morphological development) was consistently upregulated in the CelA-producing strain and exhibited co-expression with isoenzymes from the pentose phosphate pathway linked to secondary metabolism. Increased expression of these isoenzymes matches to increased fluxes in the pentose phosphate pathway. Additionally, flux maps of the central carbon metabolism show increased flux through the tricarboxylic acid cycle in the CelA-producing strain. Redirection of fluxes in the CelA-producing strain leads to higher production of NADPH, which can only partly be attributed to increased secretion. Conclusions Transcriptomic and fluxomic changes uncover potential new leads for targeted strain improvement strategies which may ease the secretion stress and metabolic burden associated with heterologous protein synthesis and secretion, and may help create a more consistently performing S. lividans strain. Yet, links to secondary metabolism and redox balancing should be further investigated to fully understand the S. lividans metabolome under heterologous protein production.

Salt tolerance in the γ-proteobacterium Halomonas elongata is linked to its ability to produce the compatible solute ectoine. The metabolism of ectoine production is of great interest since it can shed light on the biochemical basis of halotolerance as well as pave the way for the improvement of the biotechnological production of such compatible solute. Ectoine belongs to the biosynthetic family of aspartate-derived amino-acids. Aspartate is formed from oxaloacetate, thereby connecting ectoine production to the anaplerotic reactions that refill carbon into the tricarboxylic acid cycle (TCA cycle). This places a high demand on these reactions and creates the need to regulate them not only in response to growth but also in response to extracellular salt concentration. In this work, we combine modeling and experiments to analyze how these different needs shape the anaplerotic reactions in H. elongata. First, the stoichiometric and thermodynamic factors that condition the flux distributions are analyzed, then the optimal patterns of operation for oxaloacetate production are calculated. Finally, the phenotype of two deletion mutants lacking potentially relevant anaplerotic enzymes: phosphoenolpyruvate carboxylase (Ppc) and oxaloacetate decarboxylase (Oad) are experimentally characterized. The results show that the anaplerotic reactions in H. elongata are indeed subject to evolutionary pressures that differ from those faced by other gram-negative bacteria. Ectoine producing halophiles must meet a higher metabolic demand for oxaloacetate and the reliance of many marine bacteria on the Entner-Doudoroff pathway compromises the anaplerotic efficiency of Ppc, which is usually one of the main enzymes fulfilling this role. The anaplerotic flux in H. elongata is contributed not only by Ppc but also by Oad, an enzyme that has not yet been shown to play this role in vivo. Ppc is necessary for H. elongata to grow normally at low salt concentrations but it is not required to achieve near maximal growth rates as long as there is a steep sodium gradient. On the other hand, the lack of Oad presents serious difficulties to grow at high salt concentrations. This points to a shared role of these two enzymes in guaranteeing the supply of oxaloacetate for biosynthetic reactions.

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA. • This review discusses the medical application of metabolic flux analysis (MFA). • Constraint-based reconstruction and analysis (COBRA) involves metabolic simulation. • Isotope-based metabolic flux analysis (iMFA) allows more accurate flux estimation. • COBRA and iMFA have been applied to various medical problems, including cancers. • MFA bridges metabolic engineering and biomedical science as a synergistic interface. [ABSTRACT FROM AUTHOR]

Developing methylotrophic cell factories that can efficiently catalyze organic one-carbon (C1) feedstocks derived from electrocatalytic reduction of carbon dioxide into bio-based chemicals and biofuels is of strategic significance for building a carbon-neutral, sustainable economic and industrial system. With the rapid advancement of RNA sequencing technology and mass spectrometer analysis, researchers have used these quantitative microbiology methods extensively, especially isotope-based metabolic flux analysis, to study the metabolic processes initiating from C1 feedstocks in natural C1-utilizing bacteria and synthetic C1 bacteria. This paper reviews the use of advanced quantitative analysis in recent years to understand the metabolic network and basic principles in the metabolism of natural C1-utilizing bacteria grown on methane, methanol, or formate. The acquired knowledge serves as a guide to rewire the central methylotrophic metabolism of natural C1-utilizing bacteria to improve the carbon conversion efficiency, and to engineer non-C1-utilizing bacteria into synthetic strains that can use C1 feedstocks as the sole carbon and energy source. These progresses ultimately enhance the design and construction of highly efficient C1-based cell factories to synthesize diverse high value-added products. The integration of quantitative biology and synthetic biology will advance the iterative cycle of understand--design--build--testing--learning to enhance C1-based biomanufacturing in the future. [ABSTRACT FROM AUTHOR]

Background: Research about tumor “metabolic flexibility”—the ability of cells to toggle between preferred nutrients depending on the metabolic context—has largely focused on obesity-associated cancers. However, increasing evidence for a key role for nutrient competition in the tumor microenvironment, as well as for substrate regulation of immune function, suggests that substrate metabolism deserves reconsideration in immunogenic tumors that are not strongly associated with obesity. Methods: We compare two murine models: immunologically cold YUMM1.7 and immunologically-hot YUMMER1.7. We utilize stable isotope and radioisotope tracer-based metabolic flux studies as well as gas and liquid chromatography-based metabolomics analyses to comprehensively probe substrate preference in YUMM1.7 and YUMMER1.7 cells, with a subset of studies on the impact of available metabolites across a panel of five additional melanoma cell lines. We analyze bulk RNA-seq data and identify increased expression of amino acid and glucose metabolism genes in YUMMER1.7. Finally, we analyze melanoma patient RNA-seq data to identify potential prognostic predictors rooted in metabolism. Results: We demonstrate using stable isotope tracer-based metabolic flux studies as well as gas and liquid chromatography-based metabolomics that immunologically-hot melanoma utilizes more glutamine than immunologically-cold melanoma in vivo and in vitro. Analyses of human melanoma RNA-seq data demonstrate that glutamine transporter and other anaplerotic gene expression positively correlates with lymphocyte infiltration and function. Conclusions: Here, we highlight the importance of understanding metabolism in non-obesity-associated cancers, such as melanoma. This work advances the understanding of the correlation between metabolism and immunogenicity in the tumor microenvironment and provides evidence supporting metabolic gene expression as potential prognostic factors of melanoma progression and may inform investigations of adjunctive metabolic therapy in melanoma. Trial registration: Deidentified data from The Cancer Genome Atlas were analyzed. [ABSTRACT FROM AUTHOR]